Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 MiB
Average record size in memory363.6 B

Variable types

Numeric8
Text1
Categorical9

Alerts

Complain is highly overall correlated with ExitedHigh correlation
Exited is highly overall correlated with ComplainHigh correlation
RowNumber is uniformly distributedUniform
RowNumber has unique valuesUnique
CustomerId has unique valuesUnique
Tenure has 413 (4.1%) zerosZeros
Balance has 3617 (36.2%) zerosZeros

Reproduction

Analysis started2025-10-20 13:21:17.303365
Analysis finished2025-10-20 13:21:35.052465
Duration17.75 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

RowNumber
Real number (ℝ)

Uniform  Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5000.5
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-10-20T09:21:35.309806image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile500.95
Q12500.75
median5000.5
Q37500.25
95-th percentile9500.05
Maximum10000
Range9999
Interquartile range (IQR)4999.5

Descriptive statistics

Standard deviation2886.8957
Coefficient of variation (CV)0.5773214
Kurtosis-1.2
Mean5000.5
Median Absolute Deviation (MAD)2500
Skewness0
Sum50005000
Variance8334166.7
MonotonicityStrictly increasing
2025-10-20T09:21:35.854275image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
66711
 
< 0.1%
66641
 
< 0.1%
66651
 
< 0.1%
66661
 
< 0.1%
66671
 
< 0.1%
66681
 
< 0.1%
66691
 
< 0.1%
66701
 
< 0.1%
66721
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
100001
< 0.1%
99991
< 0.1%
99981
< 0.1%
99971
< 0.1%
99961
< 0.1%
99951
< 0.1%
99941
< 0.1%
99931
< 0.1%
99921
< 0.1%
99911
< 0.1%

CustomerId
Real number (ℝ)

Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15690941
Minimum15565701
Maximum15815690
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-10-20T09:21:36.153502image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum15565701
5-th percentile15578824
Q115628528
median15690738
Q315753234
95-th percentile15803034
Maximum15815690
Range249989
Interquartile range (IQR)124705.5

Descriptive statistics

Standard deviation71936.186
Coefficient of variation (CV)0.0045845681
Kurtosis-1.1961125
Mean15690941
Median Absolute Deviation (MAD)62432.5
Skewness0.0011491459
Sum1.5690941 × 1011
Variance5.1748149 × 109
MonotonicityNot monotonic
2025-10-20T09:21:36.612525image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
156346021
 
< 0.1%
156679321
 
< 0.1%
157661851
 
< 0.1%
156676321
 
< 0.1%
155990241
 
< 0.1%
157987091
 
< 0.1%
157419211
 
< 0.1%
157936711
 
< 0.1%
157979001
 
< 0.1%
157959331
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
155657011
< 0.1%
155657061
< 0.1%
155657141
< 0.1%
155657791
< 0.1%
155657961
< 0.1%
155658061
< 0.1%
155658781
< 0.1%
155658791
< 0.1%
155658911
< 0.1%
155659961
< 0.1%
ValueCountFrequency (%)
158156901
< 0.1%
158156601
< 0.1%
158156561
< 0.1%
158156451
< 0.1%
158156281
< 0.1%
158156261
< 0.1%
158156151
< 0.1%
158155601
< 0.1%
158155521
< 0.1%
158155341
< 0.1%

Surname
Text

Distinct2932
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Memory size619.6 KiB
2025-10-20T09:21:37.709745image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length23
Median length16
Mean length6.4349
Min length2

Characters and Unicode

Total characters64349
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1558 ?
Unique (%)15.6%

Sample

1st rowHargrave
2nd rowHill
3rd rowOnio
4th rowBoni
5th rowMitchell
ValueCountFrequency (%)
lo33
 
0.3%
smith32
 
0.3%
martin29
 
0.3%
scott29
 
0.3%
walker28
 
0.3%
brown26
 
0.3%
yeh25
 
0.2%
shih25
 
0.2%
genovese25
 
0.2%
maclean24
 
0.2%
Other values (2931)9779
97.3%
2025-10-20T09:21:39.044801image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a5799
 
9.0%
e5764
 
9.0%
n5235
 
8.1%
o4905
 
7.6%
i4491
 
7.0%
r3547
 
5.5%
l2921
 
4.5%
s2592
 
4.0%
u2552
 
4.0%
h2150
 
3.3%
Other values (45)24393
37.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)64349
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a5799
 
9.0%
e5764
 
9.0%
n5235
 
8.1%
o4905
 
7.6%
i4491
 
7.0%
r3547
 
5.5%
l2921
 
4.5%
s2592
 
4.0%
u2552
 
4.0%
h2150
 
3.3%
Other values (45)24393
37.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)64349
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a5799
 
9.0%
e5764
 
9.0%
n5235
 
8.1%
o4905
 
7.6%
i4491
 
7.0%
r3547
 
5.5%
l2921
 
4.5%
s2592
 
4.0%
u2552
 
4.0%
h2150
 
3.3%
Other values (45)24393
37.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)64349
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a5799
 
9.0%
e5764
 
9.0%
n5235
 
8.1%
o4905
 
7.6%
i4491
 
7.0%
r3547
 
5.5%
l2921
 
4.5%
s2592
 
4.0%
u2552
 
4.0%
h2150
 
3.3%
Other values (45)24393
37.9%

CreditScore
Real number (ℝ)

Distinct460
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean650.5288
Minimum350
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-10-20T09:21:39.347316image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum350
5-th percentile489
Q1584
median652
Q3718
95-th percentile812
Maximum850
Range500
Interquartile range (IQR)134

Descriptive statistics

Standard deviation96.653299
Coefficient of variation (CV)0.14857651
Kurtosis-0.42572568
Mean650.5288
Median Absolute Deviation (MAD)67
Skewness-0.071606608
Sum6505288
Variance9341.8602
MonotonicityNot monotonic
2025-10-20T09:21:39.638200image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850233
 
2.3%
67863
 
0.6%
65554
 
0.5%
70553
 
0.5%
66753
 
0.5%
68452
 
0.5%
67050
 
0.5%
65150
 
0.5%
68348
 
0.5%
65248
 
0.5%
Other values (450)9296
93.0%
ValueCountFrequency (%)
3505
0.1%
3511
 
< 0.1%
3581
 
< 0.1%
3591
 
< 0.1%
3631
 
< 0.1%
3651
 
< 0.1%
3671
 
< 0.1%
3731
 
< 0.1%
3762
 
< 0.1%
3821
 
< 0.1%
ValueCountFrequency (%)
850233
2.3%
8498
 
0.1%
8485
 
0.1%
8476
 
0.1%
8465
 
0.1%
8456
 
0.1%
8447
 
0.1%
8432
 
< 0.1%
8427
 
0.1%
84112
 
0.1%

Geography
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size615.4 KiB
France
5014 
Germany
2509 
Spain
2477 

Length

Max length7
Median length6
Mean length6.0032
Min length5

Characters and Unicode

Total characters60032
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFrance
2nd rowSpain
3rd rowFrance
4th rowFrance
5th rowSpain

Common Values

ValueCountFrequency (%)
France5014
50.1%
Germany2509
25.1%
Spain2477
24.8%

Length

2025-10-20T09:21:40.072942image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-20T09:21:40.371831image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
france5014
50.1%
germany2509
25.1%
spain2477
24.8%

Most occurring characters

ValueCountFrequency (%)
a10000
16.7%
n10000
16.7%
r7523
12.5%
e7523
12.5%
F5014
8.4%
c5014
8.4%
G2509
 
4.2%
m2509
 
4.2%
y2509
 
4.2%
S2477
 
4.1%
Other values (2)4954
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)60032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a10000
16.7%
n10000
16.7%
r7523
12.5%
e7523
12.5%
F5014
8.4%
c5014
8.4%
G2509
 
4.2%
m2509
 
4.2%
y2509
 
4.2%
S2477
 
4.1%
Other values (2)4954
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)60032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a10000
16.7%
n10000
16.7%
r7523
12.5%
e7523
12.5%
F5014
8.4%
c5014
8.4%
G2509
 
4.2%
m2509
 
4.2%
y2509
 
4.2%
S2477
 
4.1%
Other values (2)4954
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)60032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a10000
16.7%
n10000
16.7%
r7523
12.5%
e7523
12.5%
F5014
8.4%
c5014
8.4%
G2509
 
4.2%
m2509
 
4.2%
y2509
 
4.2%
S2477
 
4.1%
Other values (2)4954
8.3%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size604.7 KiB
Male
5457 
Female
4543 

Length

Max length6
Median length4
Mean length4.9086
Min length4

Characters and Unicode

Total characters49086
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male5457
54.6%
Female4543
45.4%

Length

2025-10-20T09:21:40.636839image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-20T09:21:40.869240image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
male5457
54.6%
female4543
45.4%

Most occurring characters

ValueCountFrequency (%)
e14543
29.6%
a10000
20.4%
l10000
20.4%
M5457
 
11.1%
F4543
 
9.3%
m4543
 
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)49086
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e14543
29.6%
a10000
20.4%
l10000
20.4%
M5457
 
11.1%
F4543
 
9.3%
m4543
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)49086
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e14543
29.6%
a10000
20.4%
l10000
20.4%
M5457
 
11.1%
F4543
 
9.3%
m4543
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)49086
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e14543
29.6%
a10000
20.4%
l10000
20.4%
M5457
 
11.1%
F4543
 
9.3%
m4543
 
9.3%

Age
Real number (ℝ)

Distinct70
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.9218
Minimum18
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-10-20T09:21:41.105475image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile25
Q132
median37
Q344
95-th percentile60
Maximum92
Range74
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.487806
Coefficient of variation (CV)0.26945841
Kurtosis1.3953471
Mean38.9218
Median Absolute Deviation (MAD)6
Skewness1.0113203
Sum389218
Variance109.99408
MonotonicityNot monotonic
2025-10-20T09:21:41.417075image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37478
 
4.8%
38477
 
4.8%
35474
 
4.7%
36456
 
4.6%
34447
 
4.5%
33442
 
4.4%
40432
 
4.3%
39423
 
4.2%
32418
 
4.2%
31404
 
4.0%
Other values (60)5549
55.5%
ValueCountFrequency (%)
1822
 
0.2%
1927
 
0.3%
2040
 
0.4%
2153
 
0.5%
2284
0.8%
2399
1.0%
24132
1.3%
25154
1.5%
26200
2.0%
27209
2.1%
ValueCountFrequency (%)
922
 
< 0.1%
881
 
< 0.1%
851
 
< 0.1%
842
 
< 0.1%
831
 
< 0.1%
821
 
< 0.1%
814
< 0.1%
803
< 0.1%
794
< 0.1%
785
0.1%

Tenure
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0128
Minimum0
Maximum10
Zeros413
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-10-20T09:21:41.684360image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q37
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8921744
Coefficient of variation (CV)0.57695786
Kurtosis-1.1652252
Mean5.0128
Median Absolute Deviation (MAD)2
Skewness0.010991458
Sum50128
Variance8.3646726
MonotonicityNot monotonic
2025-10-20T09:21:41.912177image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
21048
10.5%
11035
10.3%
71028
10.3%
81025
10.2%
51012
10.1%
31009
10.1%
4989
9.9%
9984
9.8%
6967
9.7%
10490
4.9%
ValueCountFrequency (%)
0413
 
4.1%
11035
10.3%
21048
10.5%
31009
10.1%
4989
9.9%
51012
10.1%
6967
9.7%
71028
10.3%
81025
10.2%
9984
9.8%
ValueCountFrequency (%)
10490
4.9%
9984
9.8%
81025
10.2%
71028
10.3%
6967
9.7%
51012
10.1%
4989
9.9%
31009
10.1%
21048
10.5%
11035
10.3%

Balance
Real number (ℝ)

Zeros 

Distinct6382
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76485.889
Minimum0
Maximum250898.09
Zeros3617
Zeros (%)36.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-10-20T09:21:42.164805image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median97198.54
Q3127644.24
95-th percentile162711.67
Maximum250898.09
Range250898.09
Interquartile range (IQR)127644.24

Descriptive statistics

Standard deviation62397.405
Coefficient of variation (CV)0.81580283
Kurtosis-1.4894118
Mean76485.889
Median Absolute Deviation (MAD)46766.79
Skewness-0.14110871
Sum7.6485889 × 108
Variance3.8934362 × 109
MonotonicityNot monotonic
2025-10-20T09:21:42.479359image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03617
36.2%
130170.822
 
< 0.1%
105473.742
 
< 0.1%
85304.271
 
< 0.1%
159397.751
 
< 0.1%
144238.71
 
< 0.1%
112262.841
 
< 0.1%
109106.81
 
< 0.1%
142147.321
 
< 0.1%
109109.331
 
< 0.1%
Other values (6372)6372
63.7%
ValueCountFrequency (%)
03617
36.2%
3768.691
 
< 0.1%
12459.191
 
< 0.1%
14262.81
 
< 0.1%
16893.591
 
< 0.1%
23503.311
 
< 0.1%
24043.451
 
< 0.1%
27288.431
 
< 0.1%
27517.151
 
< 0.1%
27755.971
 
< 0.1%
ValueCountFrequency (%)
250898.091
< 0.1%
238387.561
< 0.1%
222267.631
< 0.1%
221532.81
< 0.1%
216109.881
< 0.1%
214346.961
< 0.1%
213146.21
< 0.1%
212778.21
< 0.1%
212696.321
< 0.1%
212692.971
< 0.1%

NumOfProducts
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
1
5084 
2
4590 
3
 
266
4
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row2
5th row1

Common Values

ValueCountFrequency (%)
15084
50.8%
24590
45.9%
3266
 
2.7%
460
 
0.6%

Length

2025-10-20T09:21:42.731922image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-20T09:21:42.929283image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
15084
50.8%
24590
45.9%
3266
 
2.7%
460
 
0.6%

Most occurring characters

ValueCountFrequency (%)
15084
50.8%
24590
45.9%
3266
 
2.7%
460
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15084
50.8%
24590
45.9%
3266
 
2.7%
460
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15084
50.8%
24590
45.9%
3266
 
2.7%
460
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15084
50.8%
24590
45.9%
3266
 
2.7%
460
 
0.6%

HasCrCard
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
1
7055 
0
2945 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
17055
70.5%
02945
29.4%

Length

2025-10-20T09:21:43.136889image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-20T09:21:43.341239image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
17055
70.5%
02945
29.4%

Most occurring characters

ValueCountFrequency (%)
17055
70.5%
02945
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
17055
70.5%
02945
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
17055
70.5%
02945
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
17055
70.5%
02945
29.4%

IsActiveMember
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
1
5151 
0
4849 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
15151
51.5%
04849
48.5%

Length

2025-10-20T09:21:43.542274image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-20T09:21:43.715320image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
15151
51.5%
04849
48.5%

Most occurring characters

ValueCountFrequency (%)
15151
51.5%
04849
48.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15151
51.5%
04849
48.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15151
51.5%
04849
48.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15151
51.5%
04849
48.5%

EstimatedSalary
Real number (ℝ)

Distinct9999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100090.24
Minimum11.58
Maximum199992.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-10-20T09:21:43.982976image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum11.58
5-th percentile9851.8185
Q151002.11
median100193.91
Q3149388.25
95-th percentile190155.38
Maximum199992.48
Range199980.9
Interquartile range (IQR)98386.137

Descriptive statistics

Standard deviation57510.493
Coefficient of variation (CV)0.57458642
Kurtosis-1.1815184
Mean100090.24
Median Absolute Deviation (MAD)49198.15
Skewness0.0020853577
Sum1.0009024 × 109
Variance3.3074568 × 109
MonotonicityNot monotonic
2025-10-20T09:21:44.322481image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24924.922
 
< 0.1%
101348.881
 
< 0.1%
55313.441
 
< 0.1%
72500.681
 
< 0.1%
182692.81
 
< 0.1%
4993.941
 
< 0.1%
124964.821
 
< 0.1%
161971.421
 
< 0.1%
39488.041
 
< 0.1%
187811.711
 
< 0.1%
Other values (9989)9989
99.9%
ValueCountFrequency (%)
11.581
< 0.1%
90.071
< 0.1%
91.751
< 0.1%
96.271
< 0.1%
106.671
< 0.1%
123.071
< 0.1%
142.811
< 0.1%
143.341
< 0.1%
178.191
< 0.1%
216.271
< 0.1%
ValueCountFrequency (%)
199992.481
< 0.1%
199970.741
< 0.1%
199953.331
< 0.1%
199929.171
< 0.1%
199909.321
< 0.1%
199862.751
< 0.1%
199857.471
< 0.1%
199841.321
< 0.1%
199808.11
< 0.1%
199805.631
< 0.1%

Exited
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
0
7962 
1
2038 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07962
79.6%
12038
 
20.4%

Length

2025-10-20T09:21:44.637160image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-20T09:21:44.832930image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
07962
79.6%
12038
 
20.4%

Most occurring characters

ValueCountFrequency (%)
07962
79.6%
12038
 
20.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07962
79.6%
12038
 
20.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07962
79.6%
12038
 
20.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07962
79.6%
12038
 
20.4%

Complain
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
0
7956 
1
2044 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07956
79.6%
12044
 
20.4%

Length

2025-10-20T09:21:45.062357image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-20T09:21:45.274629image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
07956
79.6%
12044
 
20.4%

Most occurring characters

ValueCountFrequency (%)
07956
79.6%
12044
 
20.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07956
79.6%
12044
 
20.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07956
79.6%
12044
 
20.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07956
79.6%
12044
 
20.4%

Satisfaction Score
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
3
2042 
2
2014 
4
2008 
5
2004 
1
1932 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row5
5th row5

Common Values

ValueCountFrequency (%)
32042
20.4%
22014
20.1%
42008
20.1%
52004
20.0%
11932
19.3%

Length

2025-10-20T09:21:45.485863image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-20T09:21:45.719933image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
32042
20.4%
22014
20.1%
42008
20.1%
52004
20.0%
11932
19.3%

Most occurring characters

ValueCountFrequency (%)
32042
20.4%
22014
20.1%
42008
20.1%
52004
20.0%
11932
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
32042
20.4%
22014
20.1%
42008
20.1%
52004
20.0%
11932
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
32042
20.4%
22014
20.1%
42008
20.1%
52004
20.0%
11932
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
32042
20.4%
22014
20.1%
42008
20.1%
52004
20.0%
11932
19.3%

Card Type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size617.8 KiB
DIAMOND
2507 
GOLD
2502 
SILVER
2496 
PLATINUM
2495 

Length

Max length8
Median length7
Mean length6.2493
Min length4

Characters and Unicode

Total characters62493
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDIAMOND
2nd rowDIAMOND
3rd rowDIAMOND
4th rowGOLD
5th rowGOLD

Common Values

ValueCountFrequency (%)
DIAMOND2507
25.1%
GOLD2502
25.0%
SILVER2496
25.0%
PLATINUM2495
24.9%

Length

2025-10-20T09:21:46.027187image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-20T09:21:46.278738image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
diamond2507
25.1%
gold2502
25.0%
silver2496
25.0%
platinum2495
24.9%

Most occurring characters

ValueCountFrequency (%)
D7516
12.0%
I7498
12.0%
L7493
12.0%
O5009
8.0%
A5002
8.0%
M5002
8.0%
N5002
8.0%
G2502
 
4.0%
S2496
 
4.0%
V2496
 
4.0%
Other values (5)12477
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)62493
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D7516
12.0%
I7498
12.0%
L7493
12.0%
O5009
8.0%
A5002
8.0%
M5002
8.0%
N5002
8.0%
G2502
 
4.0%
S2496
 
4.0%
V2496
 
4.0%
Other values (5)12477
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)62493
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D7516
12.0%
I7498
12.0%
L7493
12.0%
O5009
8.0%
A5002
8.0%
M5002
8.0%
N5002
8.0%
G2502
 
4.0%
S2496
 
4.0%
V2496
 
4.0%
Other values (5)12477
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)62493
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D7516
12.0%
I7498
12.0%
L7493
12.0%
O5009
8.0%
A5002
8.0%
M5002
8.0%
N5002
8.0%
G2502
 
4.0%
S2496
 
4.0%
V2496
 
4.0%
Other values (5)12477
20.0%

Point Earned
Real number (ℝ)

Distinct785
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean606.5151
Minimum119
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-10-20T09:21:46.819448image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum119
5-th percentile255
Q1410
median605
Q3801
95-th percentile960
Maximum1000
Range881
Interquartile range (IQR)391

Descriptive statistics

Standard deviation225.92484
Coefficient of variation (CV)0.37249664
Kurtosis-1.193781
Mean606.5151
Median Absolute Deviation (MAD)195
Skewness0.008344113
Sum6065151
Variance51042.033
MonotonicityNot monotonic
2025-10-20T09:21:47.299034image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40826
 
0.3%
70925
 
0.2%
24423
 
0.2%
62923
 
0.2%
50322
 
0.2%
34322
 
0.2%
56422
 
0.2%
35122
 
0.2%
24022
 
0.2%
72021
 
0.2%
Other values (775)9772
97.7%
ValueCountFrequency (%)
1191
 
< 0.1%
1631
 
< 0.1%
2061
 
< 0.1%
21916
0.2%
2207
0.1%
22114
0.1%
22211
0.1%
22312
0.1%
2249
0.1%
22514
0.1%
ValueCountFrequency (%)
100013
0.1%
9997
 
0.1%
99812
0.1%
99715
0.1%
9962
 
< 0.1%
99519
0.2%
99417
0.2%
99312
0.1%
99213
0.1%
99111
0.1%

Interactions

2025-10-20T09:21:32.253005image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:19.309915image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:21.431333image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:23.053673image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:24.928906image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:26.677429image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:28.264341image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:29.906375image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:32.536007image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:19.656777image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:21.636394image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:23.263253image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:25.133310image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:26.868122image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:28.462345image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:30.200363image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:32.807791image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:19.929552image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:21.818139image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:23.470237image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:25.352120image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:27.088059image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:28.651519image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:30.501230image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:33.041044image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:20.184299image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:22.014716image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:23.672780image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:25.574558image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:27.276514image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:28.840616image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:30.757467image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:33.277554image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:20.464138image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:22.227213image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:24.021663image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:25.788028image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:27.493335image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:29.044868image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:31.006656image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:33.474518image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:20.668935image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:22.423670image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:24.285712image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:25.992903image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:27.681401image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:29.254007image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:31.323915image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:33.663679image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:20.865906image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:22.611128image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:24.494630image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:26.218979image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:27.871902image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:29.478409image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:31.636286image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:33.917303image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:21.237428image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:22.851137image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:24.732411image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:26.474351image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:28.084296image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:29.710038image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-10-20T09:21:31.953609image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-10-20T09:21:47.636735image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
AgeBalanceCard TypeComplainCreditScoreCustomerIdEstimatedSalaryExitedGenderGeographyHasCrCardIsActiveMemberNumOfProductsPoint EarnedRowNumberSatisfaction ScoreTenure
Age1.0000.0330.0000.373-0.0080.009-0.0020.3750.0260.0500.0130.1440.087-0.0010.0000.013-0.010
Balance0.0331.0000.0060.1390.006-0.0140.0120.1400.0000.3150.0390.0140.2300.013-0.0090.012-0.010
Card Type0.0000.0061.0000.0140.0000.0040.0000.0140.0300.0000.0000.0150.0140.0200.0040.0000.000
Complain0.3730.1390.0141.0000.0860.0230.0000.9950.1060.1750.0000.1540.3850.0070.0000.0000.023
CreditScore-0.0080.0060.0000.0861.0000.0060.0010.0860.0000.0180.0000.0250.0170.0010.0050.0000.001
CustomerId0.009-0.0140.0040.0230.0061.0000.0150.0220.0000.0000.0000.0110.006-0.0130.0040.000-0.015
EstimatedSalary-0.0020.0120.0000.0000.0010.0151.0000.0000.0210.0170.0000.0250.019-0.002-0.0060.0170.008
Exited0.3750.1400.0140.9950.0860.0220.0001.0000.1060.1730.0000.1560.3870.0000.0000.0000.022
Gender0.0260.0000.0300.1060.0000.0000.0210.1061.0000.0220.0000.0200.0420.0000.0000.0000.025
Geography0.0500.3150.0000.1750.0180.0000.0170.1730.0221.0000.0050.0180.0470.0160.0180.0000.028
HasCrCard0.0130.0390.0000.0000.0000.0000.0000.0000.0000.0051.0000.0060.0000.0000.0080.0000.026
IsActiveMember0.1440.0140.0150.1540.0250.0110.0250.1560.0200.0180.0061.0000.0380.0000.0000.0040.021
NumOfProducts0.0870.2300.0140.3850.0170.0060.0190.3870.0420.0470.0000.0381.0000.0000.0090.0000.035
Point Earned-0.0010.0130.0200.0070.001-0.013-0.0020.0000.0000.0160.0000.0000.0001.0000.0020.014-0.010
RowNumber0.000-0.0090.0040.0000.0050.004-0.0060.0000.0000.0180.0080.0000.0090.0021.0000.010-0.007
Satisfaction Score0.0130.0120.0000.0000.0000.0000.0170.0000.0000.0000.0000.0040.0000.0140.0101.0000.008
Tenure-0.010-0.0100.0000.0230.001-0.0150.0080.0220.0250.0280.0260.0210.035-0.010-0.0070.0081.000

Missing values

2025-10-20T09:21:34.232153image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-20T09:21:34.776270image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

RowNumberCustomerIdSurnameCreditScoreGeographyGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExitedComplainSatisfaction ScoreCard TypePoint Earned
0115634602Hargrave619FranceFemale4220.00111101348.88112DIAMOND464
1215647311Hill608SpainFemale41183807.86101112542.58013DIAMOND456
2315619304Onio502FranceFemale428159660.80310113931.57113DIAMOND377
3415701354Boni699FranceFemale3910.0020093826.63005GOLD350
4515737888Mitchell850SpainFemale432125510.8211179084.10005GOLD425
5615574012Chu645SpainMale448113755.78210149756.71115DIAMOND484
6715592531Bartlett822FranceMale5070.0021110062.80002SILVER206
7815656148Obinna376GermanyFemale294115046.74410119346.88112DIAMOND282
8915792365He501FranceMale444142051.0720174940.50003GOLD251
91015592389H?684FranceMale272134603.8811171725.73003GOLD342
RowNumberCustomerIdSurnameCreditScoreGeographyGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExitedComplainSatisfaction ScoreCard TypePoint Earned
9990999115798964Nkemakonam714GermanyMale33335016.6011053667.08003GOLD791
9991999215769959Ajuluchukwu597FranceFemale53488381.2111069384.71113GOLD369
9992999315657105Chukwualuka726SpainMale3620.00110195192.40005SILVER560
9993999415569266Rahman644FranceMale287155060.4111029179.52005DIAMOND715
9994999515719294Wood800FranceFemale2920.00200167773.55004PLATINUM311
9995999615606229Obijiaku771FranceMale3950.0021096270.64001DIAMOND300
9996999715569892Johnstone516FranceMale351057369.61111101699.77005PLATINUM771
9997999815584532Liu709FranceFemale3670.0010142085.58113SILVER564
9998999915682355Sabbatini772GermanyMale42375075.3121092888.52112GOLD339
99991000015628319Walker792FranceFemale284130142.7911038190.78003DIAMOND911